Image processing systems typically are used to adjust and correct image signals. For example, when printing a digitized image, such adjustments and corrections can include: color adjustment, deskewing, background and dust removal, descreening, text detection, text enhancement, color conversion, scaling and color manipulation.
In most image processing systems, digitized image signal correctors perform the adjustments or corrections based on processing parameters provided by a system operator. The task of selecting the appropriate processing parameters for these correctors to achieve certain desired output results is normally left to the operator, and is one of the more difficult tasks in image processing. As the complexity of the image processing model grows with advances in image processing technology, this task has become even more difficult.
For most adjustments or corrections, the operator typically does not want to know about the particular processing parameters being used, but instead wants to achieve the desired output results. Thus, it is desirable to determine optimal processing parameters for digitized image signal correctors automatically to achieve specified output results for an image.
Examples of previously known automatic or semi-automatic image processing systems include Spiegel et al. U.S. Pat. No. 5,615,282 and Capitant et al. U.S. Pat. No. 5,467,412. Such previously known systems, however, provide only limited image reconstruction capability. For example, such systems do not incorporate descreening or text detection facilities, and therefore an image reconstruction subsystem must be appended thereto. Further, such systems do not provide multiple data paths (e.g., for single and multiple scans) and do not support both contone and 1-bit printing.
It would be advantageous to provide improved methods and apparatus for reconstructing digitized images.